CJEE

Chinese Journal of Electrical Engineering ›› 2022, Vol. 8 ›› Issue (2): 62-74.doi: 10.23919/CJEE.2022.000015

Special Issue: Special Issue on Active Control and Protection of Future Renewables-dominated Distribution Grid

• Special Issue Papers • Previous Articles     Next Articles

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An Economic Optimization Method for Demand-side Energy-storage Accident Backup Assisted Deep Peaking of Thermal Power Units*

Wen Wei1,2,*, Ze Ye1, Yali Wang1, Shuangfeng Dai1, Lei Chen3, Xiaolong Liu4   

  1. 1. College of Economics and Management,Changsha University of Science and Technology, Changsha 410076, China;
    2. College of Economics and Management, Hunan University of Science and Technology, Yueyang 414000, China;
    3. College of Electrical and Information Engineering,Changsha University of Science and Technology, Changsha 410076, China;
    4. College of Electrical Engineering, Hunan University, Changsha 410082, China
  • Received:2022-01-07 Revised:2022-04-29 Accepted:2022-05-12 Online:2022-06-25 Published:2022-07-08
  • Contact: * E-mail: 22019057@hnist.edu.cn
  • About author:Wen Wei received the M.S. degree from the College of Economics and Management, Shenyang University of Science and Technology, Shenyang, China, in 2012. He is currently working towards his Ph.D. degree at the College of Economics and Management, Changsha University of Science and Technology, Changsha, China. His research interests include the economy and electricity price strategy of energy storage power station.
    Ze Ye received his B.S., M.S. and Ph.D. degree from Huazhong University of Science and Technology, Wuhan, China, in 1983, 1986 and 2003, respectively. From 1993 to 1994, he studied at Lock Haven University, USA. Since 2003 he has been a Professor at Changsha University of Science and Technology. His research interests include the electric power economy and electric power market optimization.
    Yali Wang received the B.S. and M.S. degrees from the College of Economics and Management, Changsha University of Science and Technology, Changsha, China, in 2006 and 2009, respectively. She is currently working towards her Ph.D. degree at the College of Economics and Management, Changsha University of Science and Technology, Changsha, China. Her research interests include the market value and pricing strategy of energy storage.
    Shuangfeng Dai received the B.S. degree from Hunan University of Science and Engineering, Yongzhou, China, in 2002, and the M.S. degree from Changsha University of Science and Technology, Changsha, China, in 2006. She is currently working towards her Ph.D. degree at the College of Economics and Management, Changsha University of Science and Technology, Changsha, China. Her research interest includes the electric power marketing.
    Lei Chen received the B.S. degree from the Jishou University, Jishou, China, in 2018, and the M.S. degree from the College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China, in 2021. He is currently working towards his Ph.D. degree at the South China University of Technology, Guangzhou, China. His research interests include the power system optimization control and operation.
    Xiaolong Liu graduated from School of Electrical Engineering, Hunan University with a Master's degree in 2017. He is currently studying for his Ph.D. at the School of Electrical Engineering, Hunan University, Changsha, China. His research interest is optimal control of energy storage power stations.
  • Supported by:
    * Philosophy and Social Science Foundation Project of Hunan Province(17JD02).

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Abstract: With the large-scale penetration of new energy such as wind power, its anti-modulation peak characteristics have increased challenges in power systems. Therefore, an economic optimization method for depth peak regulation and the depth of the emergency of the Energy storage (ES) accident on the demand side is proposed. First, a quantitative model of unplanned disconnection risk caused by weather state, load level, and fault type is constructed to obtain the spare and available ES capacity. Therefore, a deep peak regulation (DPR) economic optimization model is established to minimize the fuel injection cost of thermal power units, including ES accident standby, unit damage, and fuel demand. The particle swarm optimization algorithm is used to simulate the modified IEEE 30-node system. Based on the results, the DPR of ES accident standby can reduce the wind abandonment rate by 1.1% and the total peak adjustment cost by 33.5% under class-A weather. In class-C weather, the wind abandonment ratio can be reduced by 4.19%, reducing the cost of the total adjustment peak by 31.4%. The multiple purposes of improving the power grid modulation, wind power, and the standby utilization of ES accidents can be achieved.

Key words: Energy storage, peaking, risk quantification, economic models